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Categories / Computer Science & IT / Artificial Intelligence and Machine Learning / Artificial Intelligence and Machine Learning - Part 1

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Artificial Intelligence and Machine Learning - Part 1

Question 1 of 25 Hints left: 3

Q1

What is 'Quantile Regression'?

A A method for feature selection
B A technique for binary classification
C A regression technique that estimates conditional quantiles of the response variable
D A clustering algorithm

Q2

What is 'Naive Bayes'?

A A technique for dimensionality reduction
B A type of clustering algorithm
C A method for regression analysis
D A probabilistic classifier based on Bayes' theorem with strong independence assumptions

Q3

What does 'Naive Bayes' assume about the features?

A Independence between features
B Dependency between features
C Randomness between features
D Correlation between features

Q4

What is 'XGBoost'?

A An optimized implementation of Gradient Boosting that enhances performance
B A technique for dimensionality reduction
C A type of decision tree
D A clustering algorithm

Q5

What does 'Silhouette Score' measure?

A The accuracy of classification
B The precision of a regression model
C The effectiveness of feature selection
D The quality of clustering by comparing the similarity of points within a cluster to those in other clusters

Q6

What is 'Decision Tree' used for in machine learning?

A Performing regression analysis
B Building a model by splitting data into branches based on feature values
C Clustering data into groups
D Reducing the number of features

Q7

What does 'Feature Extraction' involve?

A Creating new features from existing data
B Handling missing values
C Normalizing the features
D Scaling the features

Q8

What does 'k-Fold Cross Validation' do?

A Tunes hyperparameters
B Normalizes the data
C Splits data into k subsets and evaluates model performance across each subset
D Selects the best features

Q9

What is 'Monte Carlo Simulation'?

A A clustering method
B A type of neural network
C A statistical technique used to model and analyze complex systems using random sampling
D A technique for data normalization

Q10

What is 'Bagging' in machine learning?

A Combining the predictions from multiple models to improve performance
B Increasing the size of the dataset
C Training a single model
D Reducing the number of features

Q11

What is the role of 'data augmentation' in machine learning?

A To increase the size and diversity of the training dataset
B To improve model interpretability
C To reduce data dimensionality
D To handle missing values

Q12

What is the main purpose of 'ensemble methods' in machine learning?

A To preprocess data
B To combine predictions from multiple models for better accuracy
C To perform feature selection
D To reduce model complexity

Q13

What is 'Grid Search'?

A A method for feature extraction
B A clustering algorithm
C A method for hyperparameter tuning by evaluating combinations of hyperparameters
D A technique for data imputation

Q14

What does 'bagging' stand for in machine learning?

A Boosted Aggregating
B Balanced Aggregating
C Bayesian Aggregating
D Bootstrap Aggregating

Q15

What is 'F1 Score'?

A The arithmetic mean of precision and recall
B The ratio of false positives to the sum of false positives and true negatives
C The ratio of true positives to the sum of true positives and true negatives
D The harmonic mean of precision and recall

Q16

What does the term 'bias' refer to in the context of machine learning?

A Performance metric
B Error due to incorrect assumptions
C Model's complexity
D Model's variance

Q17

Which metric is used to evaluate classification models based on their ability to correctly classify positive instances?

A Precision
B F1 Score
C Recall
D Accuracy

Q18

What is 'Mean Squared Error' (MSE)?

A The average of squared differences between predicted and actual values
B The sum of squared differences
C The average absolute differences between predicted and actual values
D The ratio of false positives to total instances

Q19

What does 'Feature Engineering' involve?

A Creating new features or modifying existing ones to improve model performance
B Splitting data into subsets
C Normalizing data
D Scaling features

Q20

What is the purpose of the 'test set' in machine learning?

A To evaluate the model's performance on new data
B To train the model
C To validate the model
D To preprocess the data

Q21

What is 'Anomaly Detection'?

A Classifying data into predefined categories
B Clustering similar data points
C Identifying unusual or outlier data points that do not fit the general pattern
D Reducing the dimensionality of data

Q22

What does PCA stand for in machine learning?

A Principal Component Analysis
B Parallel Component Analysis
C Primary Component Analysis
D Predictive Component Analysis

Q23

What is 'gradient descent' used for in machine learning?

A To split data into training and testing sets
B To evaluate model performance
C To select features
D To optimize model parameters

Q24

What does 'Support Vector Machine' (SVM) aim to do?

A Reduce dimensionality
B Minimize the error of a regression model
C Find the optimal hyperplane that best separates different classes
D Increase the accuracy of clustering

Q25

What is 'k-Means' used for in machine learning?

A Dimensionality reduction
B Clustering data into groups
C Classification of data
D Regression analysis

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